AI model information acquisition method and device, equipment and storage medium

Abstract

Provided in an embodiment of the present application are an AI model information acquisition method, apparatus and device, and a storage medium, the method comprising: a first network node sends a first message to a second network node, the first message being used for instructing the second network node to acquire information requested by the first network node from a specified AI model.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to an artificial intelligence (AI) model information acquisition method, device, equipment and storage medium. Background Art

[0002] In the existing 5G network, AI models can be deployed on gNB. However, for 5G wireless access network node 1 (NG-RAN node1) where AI models are deployed, if you want to make predictions based on the AI ​​models, you need to first request some information from the neighboring NG-RAN node (such as node2) as input data for the AI ​​model. However, in actual applications, there is such a situation: gNB2 has three models, namely AI model 1, model 2 and model 3, and only AI model 1 is activated; at this time, gNB1 requests prediction / measurement information from gNB2, and gNB2 may directly provide information predicted by AI model 1. At this time, the information obtained by gNB1 from gNB2 may not provide obvious gain to the AI ​​model prediction on gNB1, and the signaling overhead of information acquisition is large, and the storage and processing of the obtained information will also consume certain resources, that is, this will cause a waste of signaling overhead, storage and computing resources. Summary of the invention

[0003] In view of this, the embodiments of the present application hope to provide an AI model information acquisition method, device, equipment and storage medium.

[0004] The technical solution of the embodiment of the present application is implemented as follows:

[0005] The embodiment of the present application provides an AI model information acquisition method, which is applied to a first network node and includes:

[0006] A first message is sent to a second network node, where the first message is used to instruct the second network node to obtain information requested by the first network node from a specified AI model.

[0007] In one embodiment, before sending the first message to the second network node, the method further includes:

[0008] Sending a second message to the second network node, where the second message is used to request information related to the AI ​​model deployed by the second network node;

[0009] Receive AI model related information sent by the second network node; wherein the AI ​​model related information includes at least one of the following:

[0010] Identification information of the AI ​​model deployed by the second network node;

[0011] Status information of the AI ​​model deployed by the second network node.

[0012] The first message includes: first information and a first indicator; wherein,

[0013] The first information includes: information requested by the first network node;

[0014] The first indicator is used to indicate an AI model that provides information to the first network node.

[0015] Wherein, the first message further includes: second information and a second indicator; wherein,

[0016] The second information includes: necessary information in the information requested by the first network node;

[0017] The second indicator is used to indicate the support capability of the first network node for partial reporting.

[0018] In one embodiment, the method further comprises:

[0019] receiving a third message or a fourth message sent by the second network node; wherein,

[0020] The third message is used to indicate that the specified AI model can provide all or part of the information requested by the first network node;

[0021] The fourth message is used to indicate that the specified AI model cannot provide the information requested by the first network node.

[0022] The embodiment of the present application also provides an artificial intelligence AI model information acquisition method, which is applied to a second network node, including:

[0023] Receive a first message sent by a first network node, where the first message is used to instruct the second network node to obtain information requested by the first network node from a specified AI model.

[0024] In one embodiment, before receiving the first message sent by the first network node, the method further includes:

[0025] Receive a second message sent by the first network node, where the second message is used to request information related to the AI ​​model deployed by the second network node;

[0026] Sending AI model related information to the first network node; wherein the AI ​​model related information includes at least one of the following:

[0027] Identification information of the AI ​​model deployed by the second network node;

[0028] Status information of the AI ​​model deployed by the second network node.

[0029] The first message includes: first information and a first indicator; wherein,

[0030] The first information includes: information requested by the first network node;

[0031] The first indicator is used to indicate an AI model that provides information to the first network node.

[0032] Wherein, the first message further includes: second information and a second indicator; wherein,

[0033] The second information includes: necessary information in the information requested by the first network node;

[0034] The second indicator is used to indicate the support capability of the first network node for partial reporting.

[0035] In one embodiment, after receiving the first message sent by the first network node, the method further includes:

[0036] Determining to send the third message or the fourth message to the first network node is based on at least one of the following information:

[0037] a state of the AI ​​model providing information to the first network node;

[0038] A situation in which the AI ​​model provides the first information, or a situation in which the first information and the second information are provided;

[0039] Second indicator:

[0040] The third message is used to indicate that the designated AI model can provide all or part of the information requested by the first network node;

[0041] The fourth message is used to indicate that the specified AI model cannot provide the information requested by the first network node.

[0042] The embodiment of the present application further provides a network node, comprising: a first communication interface and a first processor; wherein:

[0043] The first communication interface is used to send a first message to the second network node, where the first message is used to instruct the second network node to obtain the information requested by the first network node from a specified AI model.

[0044] The embodiment of the present application further provides a network node, comprising: a second communication interface and a second processor; wherein:

[0045] The second communication interface is used to receive a first message sent by the first network node, where the first message is used to instruct the second network node to obtain the information requested by the first network node from a specified AI model.

[0046] The embodiment of the present application further provides a network node, comprising: a first processor and a first memory for storing a computer program that can be run on the processor,

[0047] Wherein, the first processor is used to execute the steps of the above method when running the computer program.

[0048] The embodiment of the present application further provides a network node, comprising: a second processor and a second memory for storing a computer program that can be run on the processor,

[0049] Wherein, the second processor is used to execute the steps of the above method when running the computer program.

[0050] An embodiment of the present application further provides a storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0051] The AI ​​model information acquisition method, device, equipment and storage medium provided in the embodiments of the present application, the first network node sends a first message to the second network node, and the first message is used to instruct the second network node to obtain the information requested by the first network node from the specified AI model. In the embodiment of the present application, the first network node obtains the required information (data) from the AI ​​model specified by the second network node (neighboring node), so that the unspecified AI model does not need to send data, thereby reducing the situation where the feedback data is of low value or incompatible, and at the same time saving the signaling overhead caused by processing unnecessary information. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 The following is a flow chart of the AI ​​model information acquisition method described in the embodiment of the present application: Figure 1 ;

[0053] Figure 2 The following is a flow chart of the AI ​​model information acquisition method described in the embodiment of the present application: Figure 2 ;

[0054] Figure 3 The structure of the AI ​​model information acquisition device described in the embodiment of the present application is shown as follows: Figure 1 ;

[0055] Figure 4 The structure of the AI ​​model information acquisition device described in the embodiment of the present application is shown as follows: Figure 2 ;

[0056] Figure 5 The structure of the network node described in the embodiment of the present application is shown in FIG. Figure 1 ;

[0057] Figure 6 The structure of the network node described in the embodiment of the present application is shown in FIG. Figure 2 . DETAILED DESCRIPTION

[0058] The present application is described below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the relevant inventions, rather than to limit the inventions. It should also be noted that, for ease of description, only the parts related to the relevant inventions are shown in the accompanying drawings. It should be noted that, in the absence of conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0059] The present application embodiment provides a method for obtaining AI model information, such as Figure 1 As shown, applied to a first network node, comprising:

[0060] Step 101: Send a first message to a second network node, where the first message is used to instruct the second network node to obtain information requested by the first network node from a specified AI model.

[0061] In an embodiment of the present application, in actual application, the first network node and / or the second network node may include: a 5G wireless access network node (NG-RAN node), and the first network node and the second network node are adjacent nodes to each other.

[0062] In the embodiment of the present application, before sending the first message to the second network node, the method further includes:

[0063] Sending a second message to the second network node, where the second message is used to request information related to the AI ​​model deployed by the second network node;

[0064] Receive AI model related information sent by the second network node; wherein the AI ​​model related information includes at least one of the following:

[0065] Identification information of the AI ​​model deployed by the second network node;

[0066] Status information of the AI ​​model deployed by the second network node.

[0067] In the embodiment of the present application, the first message includes: first information and a first indicator; wherein,

[0068] The first information includes: information requested by the first network node;

[0069] The first indicator is used to indicate an AI model that provides information to the first network node.

[0070] In the embodiment of the present application, the first information may include at least one of the following information used for prediction: the number of active terminals connected to the second network node, the resource status information of the second network node, the number of unlimited resource control (RRC) connections of the second network node, the terminal performance of the second network node, etc.; the terminal performance may include: average uplink and downlink throughput, average packet delay, average packet loss, etc. In actual application, the first indicator may include: a model ID of an AI model, which is used to indicate which AI model in the second network node provides information to the first network node.

[0071] In the embodiment of the present application, the first message further includes: second information and a second indicator; wherein,

[0072] The second information includes: necessary information in the information requested by the first network node;

[0073] The second indicator is used to indicate the support capability of the first network node for partial reporting.

[0074] In an embodiment of the present application, the necessary information is information that must be included in the information requested by the first network node, that is, a subset of the first information. In actual application, the necessary information may include one or more items of the first information: the number of active terminals connected to the second network node, resource status information of the second network node, the number of RRC connections of the second network node, the terminal performance of the second network node, etc., which is used to indicate the information necessary for the first network node to predict, that is, the information that the second network node must provide.

[0075] In actual application, if the AI ​​model of the first network node wants to predict whether the second network node is suitable for terminal (UE) switching (for example: whether the second network node has sufficient resources to support switching), the resource status information of the second network node must be input. If the resource status information is missing and only other information is input, the prediction accuracy will be very poor. In this case, the first network node can set the resource status information as necessary information, and the remaining information as auxiliary information, thereby further improving the prediction accuracy.

[0076] In the embodiment of the present application, the method further includes:

[0077] receiving a third message or a fourth message sent by the second network node; wherein,

[0078] The third message is used to indicate that the specified AI model can provide all or part of the information requested by the first network node;

[0079] The fourth message is used to indicate that the specified AI model cannot provide the information requested by the first network node.

[0080] In actual application, after receiving the first message, the second network node determines whether the AI ​​model specified in the second network node can provide the first information. If all the first information can be provided, a third message is sent to the first network node; if all the first information cannot be provided, only part of the information can be fed back, and the first network node supports partial reporting, a third message is sent, including which information cannot be fed back; if all the first information cannot be provided, only part of the information can be fed back, and the first network node does not support partial reporting, a fourth message is sent; if any information requested by the first network node cannot be provided temporarily, a fourth message is sent to the first network node.

[0081] It can be seen that the embodiments of the present application can obtain the status of at least one AI model of a neighboring network node through interaction between network nodes, and evaluate which neighboring network node to obtain the data the model wants based on the AI ​​model status; when a network node is deployed with multiple AI models, specify which model provides the requested data to improve the quality of acquired data and reduce the situation where the feedback data is incompatible; in the embodiments of the present application, the requested node can only feedback the data of the specified model. When the specified model is already in an inactive state, the data that can be provided by the currently activated model will not be fed back. It is necessary to determine whether the current network node supports reactivation. If it cannot be supported, no data will be fed back.

[0082] The present application also provides a method for obtaining AI model information, such as Figure 2 As shown, applied to a second network node, comprising:

[0083] Step 201: Receive a first message sent by a first network node, where the first message is used to instruct the second network node to obtain information requested by the first network node from a specified AI model.

[0084] In an embodiment of the present application, in actual application, the first network node and / or the second network node may include: a 5G wireless access network node (NG-RAN node), and the first network node and the second network node are adjacent nodes to each other.

[0085] In the embodiment of the present application, before receiving the first message sent by the first network node, the method further includes:

[0086] Receive a second message sent by the first network node, where the second message is used to request information related to the AI ​​model deployed by the second network node;

[0087] Sending AI model related information to the first network node; wherein the AI ​​model related information includes at least one of the following:

[0088] Identification information of the AI ​​model deployed by the second network node;

[0089] Status information of the AI ​​model deployed by the second network node.

[0090] In the embodiment of the present application, the first message includes: first information and a first indicator; wherein,

[0091] The first information includes: information requested by the first network node;

[0092] The first indicator is used to indicate an AI model that provides information to the first network node.

[0093] In the embodiment of the present application, the first information may include at least one of the following information used for prediction: the number of active terminals connected to the second network node, the resource status information of the second network node, the number of RRC connections of the second network node, the terminal performance of the second network node, etc.; the terminal performance may include: average uplink and downlink throughput, average packet delay, average packet loss, etc. In actual application, the first indicator may include: a model ID of an AI model, which is used to indicate which AI model in the second network node provides information to the first network node.

[0094] In the embodiment of the present application, the first message further includes: second information and a second indicator; wherein,

[0095] The second information includes: necessary information in the information requested by the first network node;

[0096] The second indicator is used to indicate the support capability of the first network node for partial reporting.

[0097] In an embodiment of the present application, the necessary information is information that must be included in the information requested by the first network node, that is, a subset of the first information. In actual application, the necessary information may include one or more items of the first information: the number of active terminals connected to the second network node, resource status information of the second network node, the number of RRC connections of the second network node, the terminal performance of the second network node, etc., which is used to indicate the information necessary for the first network node to predict, that is, the information that the second network node must provide.

[0098] In actual application, if the AI ​​model of the first network node wants to predict whether the second network node is suitable for terminal (UE) switching (for example: whether the second network node has sufficient resources to support switching), the resource status information of the second network node must be input. If the resource status information is missing and only other information is input, the prediction accuracy will be very poor. In this case, the first network node can set the resource status information as necessary information, and the remaining information as auxiliary information, thereby further improving the prediction accuracy.

[0099] In the embodiment of the present application, after receiving the first message sent by the first network node, the method further includes:

[0100] Determining to send the third message or the fourth message to the first network node is based on at least one of the following information:

[0101] a state of the AI ​​model providing information to the first network node;

[0102] A situation in which the AI ​​model provides the first information, or a situation in which the first information and the second information are provided;

[0103] Second indicator:

[0104] The third message is used to indicate that the designated AI model can provide all or part of the information requested by the first network node;

[0105] The fourth message is used to indicate that the specified AI model cannot provide the information requested by the first network node.

[0106] In one embodiment of the present application, determining, based on at least one of the above information, to send the third message or the fourth message to the first network node may include:

[0107] Determine the state of the AI ​​model that provides information to the first network node, and if the AI ​​model is in an inactive state and cannot be activated, send a fourth message to the first network node; if the AI ​​model is in an inactive state but can be activated, determine whether the AI ​​model can provide the first information, and if it cannot provide any information requested by the first network node, send a fourth message to the first network node;

[0108] If the AI ​​model is in an activated state, or in an inactivated state but the AI ​​model can be activated, if the AI ​​model can provide all the first information, sending a third message to the first network node; if the AI ​​model cannot provide all the first information, determining whether the first message includes the second information and the second indicator, if not, and the AI ​​model can only feedback partial information, if the first network node supports partial reporting, sending a third message, including which information cannot be fed back; if the first network node does not support partial reporting, sending a fourth message;

[0109] If the first message includes the second information and the second indicator, the first network node's support capability for partial reporting is determined based on the second indicator. If the first network node does not support partial reporting, a fourth message is sent. If supported, it is determined whether the AI ​​model can provide the second information. If the second information can be provided, a third message is sent to the first network node, which includes which information cannot be fed back. If the second information cannot be provided, a fourth message is sent to the first network node.

[0110] The present application is described in detail below with reference to two application examples.

[0111] Application Example 1

[0112] In this example, it is assumed that NG-RAN node 2 is deployed with three AI models: AI model 1, 2, and 3, among which model 2 is in the activated state.

[0113] Step 1: NG-RAN node 1 sends a second message to NG-RAN node 2 to obtain the model ID of the AI ​​model deployed in node 2;

[0114] Step 2: NG-RAN node 2 feeds back the model IDs of models 1, 2, and 3 it has deployed, and indicates that model 2 is in the activate state;

[0115] Step 3: NG-RAN node 1 sends a first message to NG-RAN node 2 to obtain input data for AI model prediction;

[0116] The first message includes first information, indicating that it requests information 1, and / or information 2, and / or information 3, and indicating that NG-RAN node1 requests data of model2 in node2;

[0117] Step 4: After receiving the second message, NG-RAN node 2 determines whether model 2 is still in the activated state and whether it can provide all the first information; wherein,

[0118] If model2 is still in the activated state and can provide all the first information (information 1 / information 2 / information 3), the third message is sent to node1;

[0119] If model2 is still in the activated state but cannot provide all the first information and can only feedback part of the information, and node1 supports partial reporting, then NG-RAN node2 sends a third message, which includes which information cannot be fed back;

[0120] If model2 is still in the activated state but cannot provide all the first information and can only feedback part of the information, and node1 does not support partial reporting, NG-RAN node2 sends a fourth message;

[0121] If model2 is still in the activated state but cannot provide any requested information temporarily, a fourth message is sent to node1;

[0122] If model2 is already in a non-activated state, node2 evaluates whether it can currently support the activated model2 (considering power consumption, computing power, etc.); if it cannot support the activated model2, a fourth message is sent to node1;

[0123] If the activated model2 can be supported and does not affect the use of other currently activated models, then activate model2 and determine whether it can provide the information requested by node1;

[0124] If all the first information (information 1 / information 2 / information 3) can be provided, the third message is sent to node1;

[0125] If all the first information cannot be provided and only part of the information can be fed back, and node1 supports partial reporting, NG-RANnode2 sends a third message, which includes which information cannot be fed back;

[0126] If all the first information cannot be provided and only part of the information can be fed back, and node1 does not support partial reporting, the NG-RAN node2 sends a fourth message;

[0127] If any requested information cannot be provided temporarily, a fourth message is sent to node1.

[0128] Application Example 2

[0129] In this example, it is assumed that NG-RAN node2 is deployed with three AI models, namely, AI model1, 2 and 3, where model2 is in the activated state. NG-RAN node1 sends a first message to NG-RAN node2 to obtain input data for AI model prediction.

[0130] The first message includes first information, indicating that it requests information 1, and / or information 2, and / or information 3; and indicates that NG-RAN node1 requests data of model2 in node2;

[0131] The first message also includes indicator 2 and second information, where indicator 2 indicates that it supports partial reporting; the second information includes information 1, indicating that the requested information 1 must be provided;

[0132] After receiving the first message, NG-RAN node2 determines whether model2 can provide all the first information, and if so, sends a third message to node1;

[0133] If all the first information cannot be provided, determining whether the first message includes indicator 2 and indicating that it supports partial reporting;

[0134] If partial reporting is supported, NG-RAN node2 determines whether information 1 can be provided; if it can be provided, a third message is sent to node1; if it cannot be provided, a fourth message is sent to node1.

[0135] In order to implement the method on the first network node side of the embodiment of the present application, the embodiment of the present application also provides an AI model information acquisition device, such as Figure 3 As shown, the device comprises:

[0136] The first communication unit 301 is used to send a first message to the second network node, where the first message is used to instruct the second network node to obtain the information requested by the first network node from a specified AI model.

[0137] In the embodiment of the present application, before the first communication unit 301 sends the first message to the second network node, it is also used to send a second message to the second network node, where the second message is used to request information related to the AI ​​model deployed by the second network node;

[0138] Receive AI model related information sent by the second network node; wherein the AI ​​model related information includes at least one of the following:

[0139] Identification information of the AI ​​model deployed by the second network node;

[0140] Status information of the AI ​​model deployed by the second network node.

[0141] In the embodiment of the present application, the first message includes: first information and a first indicator; wherein,

[0142] The first information includes: information requested by the first network node;

[0143] The first indicator is used to indicate an AI model that provides information to the first network node.

[0144] In the embodiment of the present application, the first message further includes: second information and a second indicator; wherein,

[0145] The second information includes: necessary information in the information requested by the first network node;

[0146] The second indicator is used to indicate the support capability of the first network node for partial reporting.

[0147] In the embodiment of the present application, the first communication unit 301 is further configured to receive a third message or a fourth message sent by the second network node; wherein:

[0148] The third message is used to indicate that the specified AI model can provide all or part of the information requested by the first network node;

[0149] The fourth message is used to indicate that the specified AI model cannot provide the information requested by the first network node.

[0150] In actual application, the first communication unit 301 can be implemented by a communication interface in the AI ​​model information acquisition device.

[0151] It should be noted that: the AI ​​model information acquisition device provided in the above embodiment only uses the division of the above program modules as an example when communicating. In actual applications, the above processing can be assigned to different program modules as needed, that is, the internal structure of the device is divided into different program modules to complete all or part of the processing described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0152] In order to implement the method on the second network node side of the embodiment of the present application, the embodiment of the present application also provides an AI model information acquisition device, such as Figure 4 As shown, the device comprises:

[0153] The second communication unit 401 is used to receive a first message sent by a first network node, where the first message is used to instruct the second network node to obtain information requested by the first network node from a specified AI model.

[0154] In the embodiment of the present application, before the second communication unit 401 receives the first message sent by the first network node, it is also used to

[0155] Receive a second message sent by the first network node, where the second message is used to request information related to the AI ​​model deployed by the second network node;

[0156] Sending AI model related information to the first network node; wherein the AI ​​model related information includes at least one of the following:

[0157] Identification information of the AI ​​model deployed by the second network node;

[0158] Status information of the AI ​​model deployed by the second network node.

[0159] In the embodiment of the present application, the first message includes: first information and a first indicator; wherein,

[0160] The first information includes: information requested by the first network node;

[0161] The first indicator is used to indicate an AI model that provides information to the first network node.

[0162] In the embodiment of the present application, the first message further includes: second information and a second indicator; wherein,

[0163] The second information includes: necessary information in the information requested by the first network node;

[0164] The second indicator is used to indicate the support capability of the first network node for partial reporting.

[0165] In the embodiment of the present application, after receiving the first message sent by the first network node, the second communication unit 401 is further configured to determine to send a third message or a fourth message to the first network node based on at least one of the following information:

[0166] a state of the AI ​​model providing information to the first network node;

[0167] A situation in which the AI ​​model provides the first information, or a situation in which the first information and the second information are provided;

[0168] Second indicator:

[0169] The third message is used to indicate that the designated AI model can provide all or part of the information requested by the first network node;

[0170] The fourth message is used to indicate that the specified AI model cannot provide the information requested by the first network node.

[0171] In actual application, the second communication unit 401 can be implemented by a communication interface in the AI ​​model information acquisition device.

[0172] It should be noted that: the AI ​​model information acquisition device provided in the above embodiment only uses the division of the above program modules as an example when communicating. In actual applications, the above processing can be assigned to different program modules as needed, that is, the internal structure of the device is divided into different program modules to complete all or part of the processing described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0173] Based on the hardware implementation of the above program module, and in order to implement the method of the first network node side of the embodiment of the present application, the embodiment of the present application also provides a (first) network node, such as Figure 5 As shown, the network node 500 includes:

[0174] The first communication interface 501 is capable of exchanging information with the second network node and / or other nodes on the network side;

[0175] A first processor 502, connected to the first communication interface 501, to implement information interaction with the second network node and / or other nodes on the network side, and configured to execute the method provided by one or more technical solutions on the network node side when running a computer program;

[0176] A first memory 503 , in which the computer program is stored.

[0177] Specifically, the first communication interface 501 is used to send a first message to the second network node, where the first message is used to instruct the second network node to obtain the information requested by the first network node from a specified AI model.

[0178] In the embodiment of the present application, before the first communication interface 501 sends the first message to the second network node, it is also used to send a second message to the second network node, where the second message is used to request information related to the AI ​​model deployed by the second network node;

[0179] Receive AI model related information sent by the second network node; wherein the AI ​​model related information includes at least one of the following:

[0180] Identification information of the AI ​​model deployed by the second network node;

[0181] Status information of the AI ​​model deployed by the second network node.

[0182] In the embodiment of the present application, the first message includes: first information and a first indicator; wherein,

[0183] The first information includes: information requested by the first network node;

[0184] The first indicator is used to indicate an AI model that provides information to the first network node.

[0185] In the embodiment of the present application, the first message further includes: second information and a second indicator; wherein,

[0186] The second information includes: necessary information in the information requested by the first network node;

[0187] The second indicator is used to indicate the support capability of the first network node for partial reporting.

[0188] In the embodiment of the present application, the first communication interface 501 is further used to receive a third message or a fourth message sent by the second network node; wherein,

[0189] The third message is used to indicate that the specified AI model can provide all or part of the information requested by the first network node;

[0190] The fourth message is used to indicate that the specified AI model cannot provide the information requested by the first network node.

[0191] It should be noted that the specific processing process of the first communication interface 501 and the first processor 502 can be understood by referring to the above method, which will not be repeated here.

[0192] Of course, in actual application, the various components in the network node 500 are coupled together through the bus system 504. It can be understood that the bus system 504 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 504 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, Figure 5 Various buses are labeled as bus system 504 .

[0193] The first memory 503 in the embodiment of the present application is used to store various types of data to support the operation of the network node 500. Examples of such data include: any computer program used to operate on the network node 500.

[0194] The method disclosed in the above embodiment of the present application can be applied to the first processor 502, or implemented by the first processor 502. The first processor 502 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the first processor 502. The above-mentioned first processor 502 may be a general-purpose processor, a digital signal processor (DSP, Digital Signal Processor), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The first processor 502 can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or any conventional processor, etc. In combination with the steps of the method disclosed in the embodiment of the present application, it can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium, which is located in the first memory 503, and the first processor 502 reads the information in the first memory 503, and completes the steps of the above method in combination with its hardware.

[0195] In an exemplary embodiment, the network node 500 may be implemented by one or more application specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers (MCUs), microprocessors, or other electronic components to execute the aforementioned method.

[0196] Based on the hardware implementation of the above program module, and in order to implement the method on the second network node side of the embodiment of the present application, the embodiment of the present application also provides a (second) network node, such as Figure 6 As shown, the network node 600 includes:

[0197] The second communication interface 601 is capable of exchanging information with the first network node and / or other nodes on the network side;

[0198] The second processor 602 is connected to the second communication interface 601 to implement information interaction with the first network node and / or other nodes on the network side, and is used to execute the method provided by one or more technical solutions on the network node side when running the computer program;

[0199] The second memory 603 , the computer program is stored in the first memory 603 .

[0200] Specifically, the second communication interface 601 is used to receive a first message sent by the first network node, where the first message is used to instruct the second network node to obtain the information requested by the first network node from a specified AI model.

[0201] In the embodiment of the present application, before the second communication interface 601 receives the first message sent by the first network node, it is also used to

[0202] Receive a second message sent by the first network node, where the second message is used to request information related to the AI ​​model deployed by the second network node;

[0203] Sending AI model related information to the first network node; wherein the AI ​​model related information includes at least one of the following:

[0204] Identification information of the AI ​​model deployed by the second network node;

[0205] Status information of the AI ​​model deployed by the second network node.

[0206] In the embodiment of the present application, the first message includes: first information and a first indicator; wherein,

[0207] The first information includes: information requested by the first network node;

[0208] The first indicator is used to indicate an AI model that provides information to the first network node.

[0209] In the embodiment of the present application, the first message further includes: second information and a second indicator; wherein,

[0210] The second information includes: necessary information in the information requested by the first network node;

[0211] The second indicator is used to indicate the support capability of the first network node for partial reporting.

[0212] In the embodiment of the present application, after receiving the first message sent by the first network node, the second communication interface 601 is further configured to determine to send a third message or a fourth message to the first network node based on at least one of the following information:

[0213] a state of the AI ​​model providing information to the first network node;

[0214] A situation in which the AI ​​model provides the first information, or a situation in which the first information and the second information are provided;

[0215] Second indicator:

[0216] The third message is used to indicate that the designated AI model can provide all or part of the information requested by the first network node;

[0217] The fourth message is used to indicate that the specified AI model cannot provide the information requested by the first network node.

[0218] It should be noted that the specific processing process of the second communication interface 601 and the second processor 602 can be understood by referring to the above method, which will not be repeated here.

[0219] Of course, in actual application, the various components in the network node 600 are coupled together through the bus system 604. It can be understood that the bus system 604 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 604 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, Figure 6 Various buses are labeled as bus system 604 .

[0220] The second memory 603 in the embodiment of the present application is used to store various types of data to support the operation of the network node 600. Examples of such data include: any computer program used to operate on the network node 600.

[0221] The method disclosed in the above embodiment of the present application can be applied to the second processor 602, or implemented by the second processor 602. The second processor 602 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the second processor 602. The above second processor 602 may be a general processor, a digital signal processor (DSP, Digital Signal Processor), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The second processor 602 can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. The general processor may be a microprocessor or any conventional processor, etc. In combination with the steps of the method disclosed in the embodiment of the present application, it can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium, which is located in the second memory 603, and the second processor 602 reads the information in the second memory 603, and completes the steps of the above method in combination with its hardware.

[0222] In an exemplary embodiment, the network node 600 may be implemented by one or more application specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers (MCUs), microprocessors, or other electronic components to execute the aforementioned method.

[0223] In an exemplary embodiment, the embodiment of the present application further provides a storage medium, namely a computer storage medium, specifically a computer-readable storage medium, for example, including a first memory 503 storing a computer program, and the computer program can be executed by the first processor 502 of the network node 500 to complete the steps of the aforementioned first network node side method. For another example, a second memory 603 storing a computer program can be executed by the second processor 602 of the network node 600 to complete the steps of the aforementioned second network node side method. The computer-readable storage medium can be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface memory, optical disk, or CD-ROM.

[0224] It should be noted that: "first", "second", etc. are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0225] In addition, the technical solutions described in the embodiments of the present application can be combined arbitrarily without conflict.

[0226] The above description is only a preferred embodiment of the present application and is not intended to limit the protection scope of the present application.

Claims

1. A method for obtaining artificial intelligence (AI) model information, characterized in that: Applied to a first network node, comprising: A first message is sent to a second network node, where the first message is used to instruct the second network node to obtain information requested by the first network node from a specified AI model.

2. The method according to claim 1, characterized in that Before sending the first message to the second network node, the method further includes: Sending a second message to the second network node, where the second message is used to request information related to the AI ​​model deployed by the second network node; Receive AI model related information sent by the second network node; wherein the AI ​​model related information includes at least one of the following: Identification information of the AI ​​model deployed by the second network node; Status information of the AI ​​model deployed by the second network node.

3. The method according to claim 1, characterized in that The first message includes: first information and a first indicator; wherein, The first information includes: information requested by the first network node; The first indicator is used to indicate an AI model that provides information to the first network node.

4. The method according to claim 3, characterized in that The first message also includes: second information and a second indicator; wherein, The second information includes: necessary information in the information requested by the first network node; The second indicator is used to indicate the support capability of the first network node for partial reporting.

5. The method according to claim 1, characterized in that The method further comprises: receiving a third message or a fourth message sent by the second network node; wherein, The third message is used to indicate that the specified AI model can provide all or part of the information requested by the first network node; The fourth message is used to indicate that the specified AI model cannot provide the information requested by the first network node.

6. A method for obtaining artificial intelligence (AI) model information, characterized in that: Applied to a second network node, comprising: Receive a first message sent by a first network node, where the first message is used to instruct the second network node to obtain information requested by the first network node from a specified AI model.

7. The method according to claim 6, characterized in that Before receiving the first message sent by the first network node, the method further includes: Receive a second message sent by the first network node, where the second message is used to request information related to the AI ​​model deployed by the second network node; Sending AI model related information to the first network node; wherein the AI ​​model related information includes at least one of the following: Identification information of the AI ​​model deployed by the second network node; Status information of the AI ​​model deployed by the second network node.

8. The method according to claim 6, characterized in that The first message includes: first information and a first indicator; wherein, The first information includes: information requested by the first network node; The first indicator is used to indicate an AI model that provides information to the first network node.

9. The method according to claim 8, characterized in that The first message also includes: second information and a second indicator; wherein, The second information includes: necessary information in the information requested by the first network node; The second indicator is used to indicate the support capability of the first network node for partial reporting.

10. The method according to claim 9, characterized in that After receiving the first message sent by the first network node, the method further includes: Determining to send the third message or the fourth message to the first network node is based on at least one of the following information: a state of the AI ​​model providing information to the first network node; A situation in which the AI ​​model provides the first information, or a situation in which the first information and the second information are provided; Second indicator: The third message is used to indicate that the designated AI model can provide all or part of the information requested by the first network node; The fourth message is used to indicate that the specified AI model cannot provide the information requested by the first network node.

11. A network node, characterized in that: include: a first communication interface and a first processor; wherein, The first communication interface is used to send a first message to the second network node, where the first message is used to instruct the second network node to obtain the information requested by the first network node from a specified AI model.

12. A network node, characterized in that: include: A second communication interface and a second processor; wherein, The second communication interface is used to receive a first message sent by the first network node, where the first message is used to instruct the second network node to obtain the information requested by the first network node from a specified AI model.

13. A network node, characterized in that: include: a first processor and a first memory for storing a computer program executable on the processor, Wherein, when the first processor is used to run the computer program, the steps of the method described in any one of claims 1 to 5 are executed.

14. A network node, characterized in that: include: a second processor and a second memory for storing a computer program executable on the processor, Wherein, when the second processor is used to run the computer program, the steps of the method described in any one of claims 6 to 10 are executed.

15. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 5, or implements the steps of the method according to any one of claims 6 to 10.

Images